Identifying aorta exit points from imaging data

ABSTRACT

A method of defining a heart region from imaging data is provided. Received imaging data is projected into a first plane. A first threshold is applied to the first plane of data to eliminate data associated with air. A largest first connected component is identified from the first threshold applied data. A first center of mass of the identified largest first connected component is calculated to define a first coordinate and a second coordinate of the heart region. The received imaging data is projected into a second plane, wherein the second plane is perpendicular to the first plane. A second threshold is applied to the second plane of data to eliminate data associated with air. A largest second connected component is identified from the second threshold applied data. A second center of mass of the identified largest second connected component is calculated to define a third coordinate of the heart region.

RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.11/562,875 filed Nov. 22, 2006, which claims priority to U.S.Provisional Patent Application No. 60/862,912, filed on Oct. 25, 2006,and titled “METHOD AND SYSTEM FOR AUTOMATIC ANALYSIS OF BLOOD VESSELSTRUCTURES AND PATHOLOGIES,” both of which are incorporated herein byreference in their entirety.

FIELD

The field of the disclosure relates generally to computer systems. Morespecifically, the disclosure relates to automatic analysis of bloodvessel structures and identification of pathologies associated with theidentified blood vessels using a computer system.

BACKGROUND

Chest pain is a common complaint in a hospital or clinic emergency room(ER). Evaluating and diagnosing chest pain remains an enormouschallenge. The ER physician generally must quickly rule out threepossible causes of the chest pain—aortic dissection (aneurysm),pulmonary embolism (PE), and myocardial infarction (coronary arterystenosis). This type of triage is known as “triple rule out.” Untilrecently, three different classes of diagnostic procedures were used inthe ER to diagnose the three potential possibilities. Today, 64-slicemulti-detector, computed tomography systems provide visualization of allthree vascular beds—the heart, the lungs, and the thoraco-abdominalaorta. Computed tomography (CT) combines the use of x-rays withcomputerized analysis of the images. Beams of x-rays are passed from arotating device through an area of interest in a patient's body fromseveral different angles to create cross-sectional images, which areassembled by computer into a three-dimensional (3-D) picture of the areabeing studied. 64-slice CT includes 64 rows of detectors, which enablethe simultaneous scan of a larger cross sectional area. Thus, 64-sliceCT provides an inclusive set of images for evaluating the three primarypotential causes of the chest pain.

Existing methods for the analysis of CT image data are semi-automaticand require a radiologist to perform a series of procedures step bystep. The radiologist analyzes blood vessels one by one by visuallyinspecting their lumen and looking for pathologies. This is a tedious,error-prone, and time consuming process. Thus, what is needed is amethod and a system for automatically identifying and locating bloodvessel pathologies. What is additionally needed is a method and a systemfor automatically quantifying a level of obstruction of a blood vessel.

SUMMARY

A method and a system for automatic computerized analysis of imagingdata is provided in an exemplary embodiment. Coronary tree branches ofthe coronary artery tree may further be labeled. The analyzed bloodvessel may be traversed to determine a location and/or size of anypathologies. A method and a system for displaying the pulmonary andcoronary artery trees and/or aorta and/or pathologies detected byanalyzing the imaging image data also may be provided in anotherexemplary embodiment. The automatic computerized analysis of imagingstudies can include any of the features described herein. Additionally,the automatic computerized analysis of imaging data can include anycombination of the features described herein.

In an exemplary embodiment, a system for defining a heart region fromimaging data is provided. The system includes, but is not limited to, animaging apparatus configured to generate imaging data in threedimensions and a processor operably coupled to the imaging apparatus toreceive the imaging data. The processor is configured to apply a firstthreshold to the first plane of data, wherein the first threshold isselected to eliminate a first pixel associated with air; to identify alargest first connected component from the first threshold applied data;to calculate a first center of mass of the identified largest firstconnected component to define a first coordinate and a secondcoordinate; to project the received imaging data into a second plane ofthe three dimensions, wherein the second plane is perpendicular to thefirst plane; to apply a second threshold to the second plane of data,wherein the second threshold is selected to eliminate a second pixelassociated with air; to identify a largest second connected componentfrom the second threshold applied data; and to calculate a second centerof mass of the identified largest second connected component to define athird coordinate. A center of a heart region is defined from the definedfirst coordinate, the defined second coordinate, and the defined thirdcoordinate. The heart region is defined using a predefined offset fromthe center of the heart region in each of the three dimensions.

In an exemplary embodiment, a device for defining a heart region fromimaging data is provided. The device includes, but is not limited to, amemory, the memory capable of storing imaging data defined in threedimensions and a processor operably coupled to the memory to receive theimaging data. The processor is configured to apply a first threshold tothe first plane of data, wherein the first threshold is selected toeliminate a first pixel associated with air; to identify a largest firstconnected component from the first threshold applied data; to calculatea first center of mass of the identified largest first connectedcomponent to define a first coordinate and a second coordinate; toproject the received imaging data into a second plane of the threedimensions, wherein the second plane is perpendicular to the firstplane; to apply a second threshold to the second plane of data, whereinthe second threshold is selected to eliminate a second pixel associatedwith air; to identify a largest second connected component from thesecond threshold applied data; and to calculate a second center of massof the identified largest second connected component to define a thirdcoordinate. A center of a heart region is defined from the defined firstcoordinate, the defined second coordinate, and the defined thirdcoordinate. The heart region is defined using a predefined offset fromthe center of the heart region in each of the three dimensions.

In another exemplary embodiment, a method of defining a heart regionfrom imaging data is provided. Received imaging data is projected into afirst plane. A first threshold is applied to the first plane of data toeliminate data associated with air. A largest first connected componentis identified from the first threshold applied data. A first center ofmass of the identified largest first connected component is calculatedto define a first coordinate and a second coordinate of the heartregion. The received imaging data is projected into a second plane,wherein the second plane is perpendicular to the first plane. A secondthreshold is applied to the second plane of data to eliminate dataassociated with air. A largest second connected component is identifiedfrom the second threshold applied data. A second center of mass of theidentified largest second connected component is calculated to define athird coordinate of the heart region. The heart region is defined usinga predefined offset from the center of the heart region in each of thethree dimensions.

In yet another exemplary embodiment, computer-readable instructions areprovided that, upon execution by a processor, cause the processor toimplement the operations of the method of defining a heart region fromimaging data.

In an exemplary embodiment, a system for labeling blood vessels fromimaging data is provided. The system includes, but is not limited to, animaging apparatus configured to generate imaging data and a processoroperably coupled to the imaging apparatus to receive the imaging data.The processor is configured to calculate a vesselness score for aplurality of voxels of imaging data; to apply a first threshold to thecalculated vesselness score of the plurality of voxels to define a firstbinary volume, wherein a voxel of the first binary volume is assigned afirst value if it is greater than the first threshold and a second valueif it is less than the first threshold; to identify a first connectedcomponent from the first binary volume; to apply a second threshold tothe calculated vesselness score for the plurality of voxels to define asecond binary volume, wherein a voxel of the second binary volume isassigned a third value if it is greater than the second threshold and afourth value if it is less than the second threshold; to identify asecond connected component from the second binary volume; and to labelthe identified first connected component with a blood vessel identifierif the first connected component intersects the identified secondconnected component.

In an exemplary embodiment, a device for labeling blood vessels fromimaging data is provided. The device includes, but is not limited to, amemory, the memory capable of storing imaging data and a processoroperably coupled to the memory to receive the imaging data. Theprocessor is configured to calculate a vesselness score for a pluralityof voxels of imaging data; to apply a first threshold to the calculatedvesselness score of the plurality of voxels to define a first binaryvolume, wherein a voxel of the first binary volume is assigned a firstvalue if it is greater than the first threshold and a second value if itis less than the first threshold; to identify a first connectedcomponent from the first binary volume; to apply a second threshold tothe calculated vesselness score for the plurality of voxels to define asecond binary volume, wherein a voxel of the second binary volume isassigned a third value if it is greater than the second threshold and afourth value if it is less than the second threshold; to identify asecond connected component from the second binary volume; and to labelthe identified first connected component with a blood vessel identifierif the first connected component intersects the identified secondconnected component.

In another exemplary embodiment, a method of labeling blood vessels fromimaging data is provided. A vesselness score is calculated for aplurality of voxels of imaging data. A first threshold is applied to thecalculated vesselness score of the plurality of voxels to define a firstbinary volume, wherein a voxel of the first binary volume is assigned afirst value if it is greater than the first threshold and a second valueif it is less than the first threshold. A first connected component isidentified from the first binary volume. A second threshold is appliedto the calculated vesselness score for the plurality of voxels to definea second binary volume, wherein a voxel of the second binary volume isassigned a third value if it is greater than the second threshold and afourth value if it is less than the second threshold. A second connectedcomponent is identified from the second binary volume. The identifiedfirst connected component is labeled with a blood vessel identifier ifthe first connected component intersects the identified second connectedcomponent.

In yet another exemplary embodiment, computer-readable instructions areprovided that, upon execution by a processor, cause the processor toimplement the operations of the method of labeling blood vessels fromimaging data.

In an exemplary embodiment, a system for identifying aorta exit pointsfrom imaging data is provided. The system includes, but is not limitedto, an imaging apparatus configured to generate imaging data and aprocessor operably coupled to the imaging apparatus to receive theimaging data. The processor is configured to identify an aorta objectfrom imaging data; to calculate a vesselness score for a plurality ofvoxels in a ring around the identified aorta object; to apply a firstthreshold to the calculated vesselness score for the plurality ofvoxels; to identify a possible exit point based on the applied firstthreshold; if greater than one possible exit point is identified, toidentify a closest possible exit point to the aorta object; and ifgreater than one closest possible exit point is identified, to identifyan exit point by selecting the closest possible exit point having amaximum width.

In an exemplary embodiment, a device for identifying aorta exit pointsfrom imaging data is provided. The device includes, but is not limitedto, a memory, the memory capable of storing imaging data and a processoroperably coupled to the memory to receive the imaging data. Theprocessor is configured to identify an aorta object from imaging data;to calculate a vesselness score for a plurality of voxels in a ringaround the identified aorta object; to apply a first threshold to thecalculated vesselness score for the plurality of voxels; to identify apossible exit point based on the applied first threshold; if greaterthan one possible exit point is identified, to identify a closestpossible exit point to the aorta object; and if greater than one closestpossible exit point is identified, to identify an exit point byselecting the closest possible exit point having a maximum width.

In another exemplary embodiment, a method of identifying aorta exitpoints from imaging data is provided. An aorta object is identified fromimaging data. A vesselness score is calculated for a plurality of voxelsin a ring around the identified aorta object. A first threshold isapplied to the calculated vesselness score for the plurality of voxels.A possible exit point is identified based on the applied firstthreshold. If greater than one possible exit point is identified, aclosest possible exit point is identified to the aorta object. Ifgreater than one closest possible exit point is identified, an exitpoint is identified by selecting the closest possible exit point havinga maximum width.

In yet another exemplary embodiment, computer-readable instructions areprovided that, upon execution by a processor, cause the processor toimplement the operations of the method of identifying aorta exit pointsfrom imaging data.

In an exemplary embodiment, a system for creating a blood vessel treefrom imaging data is provided. The system includes, but is not limitedto, an imaging apparatus configured to generate imaging data and aprocessor operably coupled to the imaging apparatus to receive theimaging data. The processor is configured (a) to receive a blood vesselobject identified from computed tomography (CT) data; (b) to select astarting point of the received blood vessel object; (c) to calculate awidth map for the received blood vessel object; (d) to identify an exitpoint for the received blood vessel object; (e) to calculate a distancemap relative to the identified exit point for the received candidatevessel object, wherein the distance map is weighted by the width valuesof the width map; (f) to identify an endpoint during calculation of thedistance map; (g) to backtrack from the identified endpoint to theselected starting point to define a path in a vessel tree; and (h) torepeat (g) for each identified endpoint to create a vessel tree for theblood vessel object.

In an exemplary embodiment, a device for creating a blood vessel treefrom imaging data is provided. The device includes, but is not limitedto, a memory, the memory capable of storing imaging data and a processoroperably coupled to the memory to receive the imaging data. Theprocessor is configured (a) to receive a blood vessel object identifiedfrom computed tomography (CT) data; (b) to select a starting point ofthe received blood vessel object; (c) to calculate a width map for thereceived blood vessel object; (d) to identify an exit point for thereceived blood vessel object; (e) to calculate a distance map relativeto the identified exit point for the received candidate vessel object,wherein the distance map is weighted by the width values of the widthmap; (f) to identify an endpoint during calculation of the distance map;(g) to backtrack from the identified endpoint to the selected startingpoint to define a path in a vessel tree; and (h) to repeat (g) for eachidentified endpoint to create a vessel tree for the blood vessel object.

In another exemplary embodiment, a method of creating a blood vesseltree from imaging data is provided. The method includes but is notlimited to, (a) receiving a blood vessel object identified from computedtomography (CT) data; (b) selecting a starting point of the receivedblood vessel object; (c) calculating a width map for the received bloodvessel object; (d) identifying an exit point for the received bloodvessel object; (e) calculating a distance map relative to the identifiedexit point for the received candidate vessel object, wherein thedistance map is weighted by the width values of the width map; (f)identifying an endpoint during calculation of the distance map; (g)backtracking from the identified endpoint to the selected starting pointto define a path in a vessel tree; and (h) repeating (g) for eachidentified endpoint to create a vessel tree for the blood vessel object.

In yet another exemplary embodiment, computer-readable instructions areprovided that, upon execution by a processor, cause the processor toimplement the operations of the method of creating a blood vessel treefrom imaging data.

Other principal features and advantages of the invention will becomeapparent to those skilled in the art upon review of the followingdrawings, the detailed description, and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention will hereafter be described withreference to the accompanying drawings, wherein like numerals willdenote like elements.

FIG. 1 depicts a block diagram of an automated CT image processingsystem in accordance with an exemplary embodiment.

FIGS. 2 a and 2 b depict a flow diagram illustrating exemplaryoperations performed by the automated CT image processing system of FIG.1 in accordance with an exemplary embodiment.

FIG. 3 depicts a flow diagram illustrating exemplary operationsperformed in detecting a heart region in accordance with an exemplaryembodiment.

FIG. 4 depicts a flow diagram illustrating exemplary operationsperformed in detecting a lung region in accordance with an exemplaryembodiment.

FIGS. 5 a and 5 b depict a flow diagram illustrating exemplaryoperations performed in detecting and identifying the aorta inaccordance with an exemplary embodiment.

FIGS. 6 a and 6 b depict a flow diagram illustrating exemplaryoperations performed in identifying coronary artery vessel exit pointsfrom the aorta in accordance with an exemplary embodiment.

FIGS. 7 a and 7 b depict a flow diagram illustrating exemplaryoperations performed in identifying coronary artery vessel tree inaccordance with an exemplary embodiment.

FIG. 8 depicts a flow diagram illustrating exemplary operationsperformed in identifying calcium seed in accordance with an exemplaryembodiment.

FIG. 9 depicts a flow diagram illustrating exemplary operationsperformed in identifying in identifying soft plaque in accordance withan exemplary embodiment.

FIG. 10 depicts a graph illustrating X-junction removal from a coronaryartery vessel tree in accordance with an exemplary embodiment.

FIG. 11 depicts a first user interface of a visualization applicationpresenting summary pathology results in accordance with an exemplaryembodiment.

FIG. 12 depicts a second user interface of the visualization applicationpresenting multiple views of a pathology in accordance with a firstexemplary embodiment.

FIG. 13 depicts a third user interface of the visualization applicationpresenting a blood vessel effective lumen area as a function of distancefrom the aorta in accordance with an exemplary embodiment.

FIG. 14 depicts a fourth user interface of the visualization applicationpresenting multiple views of a pathology in accordance with a secondexemplary embodiment.

FIG. 15 depicts a fifth user interface of the visualization applicationpresenting multiple views of a pathology in accordance with a thirdexemplary embodiment.

DETAILED DESCRIPTION

With reference to FIG. 1, a block diagram of an image processing system100 is shown in accordance with an exemplary embodiment. Imageprocessing system 100 may include a CT apparatus 101 and a computingdevice 102. Computing device 102 may include a display 104, an inputinterface 106, a memory 108, a processor 110, a pathology identificationapplication 112, and a visualization application 114. In the embodimentillustrated in FIG. 1, CT apparatus 101 generates image data. Ingeneral, however, the present techniques are well-suited for use with awide variety of medical diagnostic system modalities, including magneticresonance imaging systems, ultrasound systems, positron emissiontomography systems, nuclear medicine systems, etc. Moreover, the variousmodality systems may be of a different type, manufacture, and model.Thus, different and additional components may be incorporated intocomputing device 102. Components of image processing system 100 may bepositioned in a single location, a single facility, and/or may be remotefrom one another. As a result, computing device 102 may also include acommunication interface, which provides an interface for receiving andtransmitting data between devices using various protocols, transmissiontechnologies, and media as known to those skilled in the art. Thecommunication interface may support communication using varioustransmission media that may be wired or wireless.

Display 104 presents information to a user of computing device 102 asknown to those skilled in the art. For example, display 104 may be athin film transistor display, a light emitting diode display, a liquidcrystal display, or any of a variety of different displays known tothose skilled in the art now or in the future.

Input interface 106 provides an interface for receiving information fromthe user for entry into computing device 102 as known to those skilledin the art. Input interface 106 may use various input technologiesincluding, but not limited to, a keyboard, a pen and touch screen, amouse, a track ball, a touch screen, a keypad, one or more buttons, etc.to allow the user to enter information into computing device 102 or tomake selections presented in a user interface displayed on display 104.Input interface 106 may provide both an input and an output interface.For example, a touch screen both allows user input and presents outputto the user.

Memory 108 is an electronic holding place or storage for information sothat the information can be accessed by processor 110 as known to thoseskilled in the art. Computing device 102 may have one or more memoriesthat use the same or a different memory technology. Memory technologiesinclude, but are not limited to, any type of RAM, any type of ROM, anytype of flash memory, etc. Computing device 102 also may have one ormore drives that support the loading of a memory media such as a compactdisk or digital video disk.

Processor 110 executes instructions as known to those skilled in theart. The instructions may be carried out by a special purpose computer,logic circuits, or hardware circuits. Thus, processor 110 may beimplemented in hardware, firmware, software, or any combination of thesemethods. The term “execution” is the process of running an applicationor the carrying out of the operation called for by an instruction. Theinstructions may be written using one or more programming language,scripting language, assembly language, etc. Processor 110 executes aninstruction, meaning that it performs the operations called for by thatinstruction. Processor 110 operably couples with display 104, with inputinterface 106, with memory 108, and with the communication interface toreceive, to send, and to process information. Processor 110 may retrievea set of instructions from a permanent memory device and copy theinstructions in an executable form to a temporary memory device that isgenerally some form of RAM. Computing device 102 may include a pluralityof processors that use the same or a different processing technology.

Pathology identification application 112 performs operations associatedwith analysis of blood vessel structures and with identification ofpathologies associated with the analyzed blood vessels. Some or all ofthe operations and interfaces subsequently described may be embodied inpathology identification application 112. The operations may beimplemented using hardware, firmware, software, or any combination ofthese methods. With reference to the exemplary embodiment of FIG. 1,pathology identification application 112 is implemented in softwarestored in memory 108 and accessible by processor 110 for execution ofthe instructions that embody the operations of pathology identificationapplication 112. Pathology identification application 112 may be writtenusing one or more programming languages, assembly languages, scriptinglanguages, etc. Pathology identification application 112 may integratewith or otherwise interact with visualization application 114.

Visualization application 114 performs operations associated withpresentation of the blood vessel analysis and identification results toa user. Some or all of the operations and interfaces subsequentlydescribed may be embodied in visualization application 114. Theoperations may be implemented using hardware, firmware, software, or anycombination of these methods. With reference to the exemplary embodimentof FIG. 1, visualization application 114 is implemented in softwarestored in memory 108 and accessible by processor 110 for execution ofthe instructions that embody the operations of visualization application114. Visualization application 114 may be written using one or moreprogramming languages, assembly languages, scripting languages, etc.

CT apparatus 101 and computing device 102 may be integrated into asingle system such as a CT imaging machine. CT apparatus 101 andcomputing device 102 may be connected directly. For example, CTapparatus 101 may connect to computing device 102 using a cable fortransmitting information between CT apparatus 101 and computing device102. CT apparatus 101 may connect to computing device 102 using anetwork. In an exemplary embodiment, computing device 102 is connectedto a hospital computer network and a picture archive, and communicationsystem (PACS) receives a CT study acquired on CT apparatus 101 in an ER.Using PACS, CT images are stored electronically and accessed usingcomputing device 102. CT apparatus 101 and computing device 102 may notbe connected. Instead, the CT study acquired on CT apparatus 101 may bemanually provided to computing device 102. For example, the CT study maybe stored on electronic media such as a CD or a DVD. After receiving theCT study, computing device 102 may start automatic processing of the setof images that comprise the CT study. In an exemplary embodiment, CTapparatus 101 is a 64-slice multi-detector advanced CT scanner having areconstructed slice width and inter-slice distance less than or equal toapproximately 0.5 millimeters (mm), which produces standard digitalimaging and communications in medicine (DICOM) images. Computing device102 may be a computer of any form factor.

Image processing system 100 may provide an initial classification anddecision support system, which allows fast and accurate ruling out ofthe three major diseases associated with chest pain. Image processingsystem 100 can be provided as a primary CT study inspection toolassisting an ER physician. Additionally, image processing system 100 canbe used either to completely rule out some or all of the three diseases(in case of negative results) or as a trigger to call a radiologistand/or a cardiologist to further analyze the case. Additionally, imageprocessing system 100 may automatically identify and segment bloodvessel trees and automatically analyze each blood vessel to detect andmap all relevant pathologies, including calcified and soft plaquelesions and degree of stenosis.

With reference to FIGS. 2 a and 2 b, exemplary operations associatedwith pathology identification application 112 and visualizationapplication 114 of FIG. 1 are described. Additional, fewer, or differentoperations may be performed, depending on the embodiment. The order ofpresentation of the operations is not intended to be limiting. In anoperation 200, pathology identification application 112 receives CTimage data. The CT image data may be received from CT apparatus 101directly or using a network. The CT image data also may be receivedusing a memory medium. In an operation 202, a heart region is identifiedfrom the received CT image data. Data associated with the identifiedheart region is stored at computing device 102. In an exemplaryembodiment, the data associated with the identified heart regionincludes a heart bounding box.

With reference to FIG. 3, exemplary operations associated withidentifying the heart region data are described in accordance with anexemplary embodiment. Additional, fewer, or different operations may beperformed, depending on the embodiment. The order of presentation of theoperations is not intended to be limiting. For larger studies that mayinclude head and neck or abdominal regions, a determination of the heartregion provides a correct anatomical starting point for furthersegmentation. For smaller studies, a determination of the heart regionreduces the size of the processed region and removes the non-relevantareas to reduce the false alarm risk. In an exemplary embodiment, thetop and bottom boundaries of the heart region are cut a predetermineddistance above and below the heart center.

In an operation 300, a first projection into an X-Y plane is defined. Apositive X-axis is defined as extending out from the left side of thebody. A positive Y-axis is defined as extending out from the back sideof the body. A positive Z-axis is defined as extending out from the headof the body. The first projection is defined by summing the DICOM seriesalong the Z-axis. In an operation 302, a threshold is applied to thefirst projection. For example, a threshold greater than approximatelyzero may be applied to eliminate the negative values of air whichdominate the region outside the heart region and to retain the positiveHounsfeld unit (HU) values which include the fat, blood, and boneswithin the heart region. In an operation 304, a first largest connectcomponent (CC) is identified in the thresholded first projection. In anoperation 306, a first center of mass of the first largest CC isdetermined and denoted as X_(c), Y_(c1).

In an operation 308, a second projection into a Y-Z plane is defined.The second projection is defined by summing the DICOM series along theX-axis. In an operation 310, a threshold is applied to the secondprojection data. For example, a threshold greater than approximatelyzero may be applied to eliminate the negative values of air whichdominate the region outside the heart region and to retain the positiveHU values which include the fat, blood, and bones within the heartregion. In an operation 312, a second largest CC is identified in thethresholded second projection data. In an operation 314, a second centerof mass of the second largest CC is determined and denoted as Y_(c2),Z_(c). A heart region center is defined as X_(c), Y_(c1), Z_(c). In anoperation 316, a heart region bounding box is defined from the heartregion center and an average heart region width in each axis direction,W_(X), W_(Y), W_(Z). In an operation 318, the defined heart regionbounding box is stored at computing device 102. The X-axis, Y-axis,Z-axis system centered at X_(c), Y_(c1), Z_(c) defines a body coordinatesystem.

With reference again to FIG. 2, in an operation 204, a lung region isidentified from the received CT image data. Data associated with theidentified lung region is stored at computing device 102. In anexemplary embodiment, the data associated with the identified lungregion may include a lung mask. With reference to FIG. 4, exemplaryoperations associated with identifying the lung region data aredescribed in accordance with an exemplary embodiment. Additional, fewer,or different operations may be performed, depending on the embodiment.The order of presentation of the operations is not intended to belimiting. In an operation 400, a lung threshold is applied to each sliceof the DICOM series data. For example, a lung threshold of −400 HU maybe applied. In an operation 402, a morphological filter is applied tothe binary image. In an exemplary embodiment, the binary image isfiltered using a morphological closing operation to define a lung regionin the CT image data. Other processes for filling holes in the image maybe used as known to those skilled in the art. In an operation 404, thedefined lung region is stored at computing device 102.

With reference again to FIG. 2, in an operation 206, a thorax region isidentified from the received CT image data. Data associated with theidentified thorax region is stored at computing device 102. The thoraxregion may be defined as a convex hull of the lungs and the diaphragm.In an operation 208, the pulmonary arteries are identified from thereceived CT image data. Data associated with the identified pulmonaryarteries is stored at computing device 102.

In an operation 210, the received CT image data is preprocessed. Forexample, preprocessing may include image enhancement, smoothing, noisereduction, acquisition artifacts detection, etc. Examples of imageenhancement algorithms include Gaussian smoothing, median filtering,bilateral filtering, anisotropic diffusion, etc.

In an operation 214, the left and right main pulmonary artery trees aredefined. In an operation 216, the lumen of the left and right mainpulmonary artery trees is analyzed to identify any pulmonary embolismcandidates. In an operation 218, any pulmonary embolism candidates areclassified. In an operation 220, possible pulmonary embolism lesions areidentified.

In an operation 222, the ascending and the visible part of the abdominalaorta are segmented. In an operation 224, the lumen of the abdominalaorta is segmented. In an operation 226, a 3-D geometry of the abdominalaorta is modeled. In an operation 228, the modeled aorta is compared toa hypothesized “normal” abdominal aorta to identify deviations from thehypothesized “normal” abdominal aorta. In an operation 230, suspiciouslocations are detected and analyzed to identify dissections andaneurysms.

In an operation 238, the aorta is identified. The aorta is detected inthe first imaging slice of the heart region bounding box based, forexample, on intensity and shape properties including circularity,compactness, and area. The remainder of the aorta is identified bymoving from slice to slice and looking for a similar 2-D object in eachslice. Data associated with the identified aorta is stored at computingdevice 102. In an exemplary embodiment, the data associated with theidentified aorta includes an aorta shape and boundary in the identifiedheart region. It is assumed that the heart region bounding box detectedon the previous step includes the aorta exit from the heart and that thecross section of the aorta in the upper slice of the heart region isapproximately circular.

With reference to FIGS. 5 a and 5 b, exemplary operations associatedwith identifying the aorta are described in accordance with an exemplaryembodiment. Additional, fewer, or different operations may be performed,depending on the embodiment. The order of presentation of the operationsis not intended to be limiting. In an operation 500, a first slice isselected from the heart region data. In an operation 502, an aortathreshold is applied to the first slice of the DICOM series data. Forexample, a lung threshold of 200 HU may be used. In an operation 504, amorphological filter is applied to the binary image. In an exemplaryembodiment, the binary image is filtered using a series of morphologicalfiltering operators including an opening operator using a firstparameter. In an operation 506, one or more CCs are identified.

In an operation 508, a compactness of each identified CC is determined.In an operation 510, identified CCs having a determined compactness thatexceeds a compactness threshold are eliminated from furtherconsideration. A compactness measure of a shape is a ratio of the areaof the shape to the area of a circle (the most compact shape) having thesame perimeter. The ratio may be expressed mathematically asM=4π(area)/2(perimeter). In an exemplary embodiment, the compactnessthreshold is 0.75. In an operation 512, a size of each identifiedconnected component is determined. In an operation 514, identified CCshaving a size that exceeds a maximum size threshold or that is below aminimum size threshold are eliminated from further consideration. In anexemplary embodiment, the maximum size threshold is 10,000 pixels. In anexemplary embodiment, the minimum size threshold is 1,000 pixels. In anoperation 516, a determination is made concerning whether or not anyidentified CCs remain for consideration. If identified CCs remain forconsideration, processing continues at an operation 518. If noidentified CCs remain for consideration, processing continues at anoperation 520. In operation 518, an initial aorta candidate is selectedfrom the remaining CCs. For example, if a plurality of identified CCsremain for consideration, the largest candidate CC that is foremost inthe body is selected as the initial aorta candidate.

In operation 520, a next slice is selected from the heart region data.In an operation 522, the aorta threshold is applied to the next slice ofthe DICOM series data. In an operation 524, the morphological filter isapplied to the binary image. In an operation 526, one or more CCs areidentified. In an operation 528, a compactness of each identified CC isdetermined. In an operation 530, the identified CCs having a determinedcompactness that exceeds the compactness threshold are eliminated fromfurther consideration. In an operation 532, a size of each identifiedconnected component is determined. In an operation 534, the identifiedCCs having a size that exceeds the maximum size threshold or that isbelow the minimum size threshold are eliminated from furtherconsideration. In an operation 536, a determination is made concerningwhether or not any identified CCs remain for consideration in thecurrent slice. If identified CCs remain for consideration, processingcontinues at an operation 538. If no identified CCs remain forconsideration, processing continues at operation 520.

In operation 538, the identified CCs from the current slice are comparedwith the aorta candidate object(s) created from the previous slice(s).In an operation 540, a determination is made concerning whether or notany identified CCs match CCs identified from the previous slices. In anoperation 542, if a match is found between a CC and an aorta candidateobject, the matched CC is assigned to the aorta candidate object. Forexample, if a center of a CC is closer than twenty pixels to the centerof an aorta candidate object, the CC may be identified as matched withthe aorta candidate object. In an operation 544, if a match is not foundbetween a CC and an aorta candidate object, a new aorta candidate objectis created based on the CC.

In an operation 546, a determination is made concerning whether or notall of the slices have been processed. If slices remain, processingcontinues at operation 520. If no slices remain, in an operation 548,aorta candidate objects are eliminated based on length. For example,aorta candidate objects that persist for less than 20 slices may beremoved from further consideration. In an operation 550, an aorta objectis selected from the remaining aorta candidate objects. For example, theaorta candidate object closest to the upper left corner of the image maybe selected as the aorta object. In an operation 552, a bounding box isdefined around the selected aorta object to identify a region in whichthe aorta is located in the CT image data. In an operation 554, theselected aorta object is stored at computing device 102.

With reference again to FIG. 2, in an operation 240, exit points of thecoronary arteries from the aorta are identified by evaluating allstructures connected to the aorta object which look like a vessel. Thedirection of the vessel near a link point with the aorta object shouldbe roughly perpendicular to an aorta centerline. Additionally, the leftand right coronary arteries are expected to exit from the aorta objectin a certain direction relative to the body coordinate system. If thereare several exit point candidates, the exit point candidate which leadsto a larger blood vessel tree is selected. It is assumed that theselected aorta object includes the points where the left and rightcoronary trees connect with the aorta.

With reference to FIG. 6, exemplary operations associated withidentifying the exit points of the coronary arteries from the aortaobject are described in accordance with an exemplary embodiment.Additional, fewer, or different operations may be performed, dependingon the embodiment. The order of presentation of the operations is notintended to be limiting. Imaging slices including the aorta bounding boxand a mask of the aorta detected at a previous slice are processed todetect the aorta at the current slice. In an operation 600, a firstslice is selected from the aorta object. In an operation 602, regionsare segmented based on a segmentation threshold at the detected aortaedges. The segmentation threshold may be calculated from the medianvalue of pixels of the smoothed image at the detected edges. A ring ofpre-defined radius is defined around the aorta edges detected on theprevious slice and the edges are found in the ring on the current slice.Small edges are removed from further consideration. The segmentationthreshold may be selected adaptively. For example, if no edges are foundin the ring using the calculated segmentation threshold, the calculatedsegmentation threshold is reduced by half, and the procedure isrepeated. If no edges are found using the lowered segmentationthreshold, the calculated segmentation threshold is used for subsequentslices.

In an operation 604, the segmented image is post-processed. For example,small segmented objects are removed, possible vessels are removed fromthe segmented aorta candidates, and the segmented aorta candidates areintersected with the aorta detected in the previous slice. In anoperation 606, the aorta candidates are validated by ensuring that thereis at least one candidate that intersected the aorta detected in theprevious slice and by ensuring that the aorta does not grow too fast.For example, if the aorta size in both a previous and a current slice islarger than 1500 pixels, the size growth ratio may be limited to 1.4. Inan operation 608, the aorta candidates are selected. For example, CCswith a small intersection with the previously detected aorta are removedfrom consideration, and the upper-left-most candidate is chosen if aplurality of aorta candidates exist in the current slice. In anoperation 610, the compactness of the selected aorta candidate ischecked to ensure that the candidate is not compact. If the aortacandidate is not compact, the aorta search window is limited for thenext slice. If the aorta candidate is compact, the whole image is usedto search for the aorta in the next slice. In an operation 612, abounding box for the aorta is calculated. If the aorta candidate is notcompact, the bounding box size may be fixed and only the position of thebounding box updated to compensate for aorta movement. If the aortacandidate is compact, the bounding box may be attached to the upper leftside of the aorta.

In an operation 614, a vesselness score is calculated for each voxel ofthe aorta object. As known to those skilled in the art, the vesselnessscore can be determined using a vesselness function. A vesselnessfunction is a widely used function based on the analysis of Hessianeigen values. A good description of an exemplary vesselness function canbe found for example in Frangi, A. F., Niessen, W. J., Vincken, K. L.and Viergever, M. A., 1998, “Multiscale Vessel Enhancement Filtering”,MICCAI'98, LNCS 1496, pp. 130-137. In an operation 616, a vesselnessthreshold is applied to the calculated vesselness score to identifypossible exit points based on the HU value of a ring around the aortaobject. Pixels in a ring around the detected aorta are grouped into CCs,which are analyzed to choose the most probable candidates for coronarytree exit points. In an operation 618, possible exit points are filteredto remove false candidates. For example, the possible exit points may befiltered based on a size of the CC corresponding to the possible exit, alocation of the CC relative to the aorta, an incident angle of the CCrelative to the aorta, etc. In an operation 620, a determination is madeconcerning whether or not any exit points are left. If no exit pointsare left for this slice, processing continues at an operation 624. Ifone or more exit points are left for this slice, processing continues atan operation 622. In operation 622, the one or more exit points left forthis slice are added to a possible exit points list. In an operation624, a determination is made whether or not the last slice has beenprocessed. If the last slice has not been processed, processingcontinues at an operation 626. In operation 626, the next slice isselected from the aorta object data and processing continues atoperation 602.

If the last slice has been processed, processing continues at anoperation 628. In operation 628, a CC is identified for each exit pointincluded in the possible exit points list. In an operation 630, a volumeis calculated for each exit point CC (EPCC). In an operation 632, anyEPCC having a volume below a volume threshold is eliminated from furtherconsideration as an exit point. For example, the volume threshold may be1500 voxels. In an operation 634, a maximum width of each EPCC iscalculated. In an operation 636, any EPCC having a maximum width below awidth threshold is eliminated from further consideration as an exitpoint. For example, the width threshold may be 2 mm. In an operation638, a distance to the aorta is calculated for each EPCC. In anoperation 640, the EPCC having a minimum distance to the aorta isselected. In an operation 642, a determination is made concerningwhether or not a plurality of EPCCs remain. If a plurality of EPCCsremain, processing continues at an operation 644. If a plurality ofEPCCs do not remain, processing continues at an operation 646. Inoperation 644, the EPCC having a maximum width is selected from theplurality of EPCCs remaining. In operation 646, the exit point isidentified from the selected EPCCs.

With reference again to FIG. 2, in an operation 242, a coronary arteryvessel tree is defined. For a traversed section of the blood vesseltree, a set of end points is identified, and an attempt is made tocontinue tracking beyond the end point in the direction of thecorresponding tree branch. If an additional tree segment is detected, itis connected to the traversed tree, and the processing continuesrecursively. The process is finished when no branch can be continued.The stopping condition may result in connecting a wrong structure to thecoronary tree (e.g. a vein or some debris in a noisy CT study). As aresult, the successfully tracked vessels are identified and those whichremain to be detected are identified. For example, which blood vesselsare to be segmented (e.g. RCA, LM, LAD, LCX and others) may be definedas an input to the process. Additionally, a maximum vessel length totrack (e.g. 5 cm from the aorta) and a minimum blood vessel diameter tocontinue tracking also may be defined as inputs to the process. Thelocation of some blood vessels may be based on anatomical landmarks. Forexample, the RCA goes in the right atrioventricular plane. Theseanatomical landmarks, which may be collated through an anatomical priorsprocessing operation, allow false structures in the “wrong” places to bediscarded and support the location of lost branches in the “right”places. A graph representation of the segmented vessel tree can be builtfrom the identified end points and bifurcation points. Graph nodes arethe end points and the bifurcation points. The edges are segments of avessel centerline between the nodes.

With reference to FIGS. 7 a and 7 b, exemplary operations associatedwith defining the coronary artery vessel tree are described inaccordance with an exemplary embodiment. Additional, fewer, or differentoperations may be performed, depending on the embodiment. The order ofpresentation of the operations is not intended to be limiting. In anoperation 700, the vesselness score data is received. In an operation702, a first binary volume is defined for a first threshold. The firstbinary volume includes a ‘1’ for each voxel that exceeds the firstthreshold and a ‘0’ for each voxel that does not exceed the firstthreshold. In an operation 704, a second binary volume is defined for asecond threshold. The second binary volume includes a ‘1’ for each voxelthat exceeds the second threshold and a ‘0’ for each voxel that does notexceed the second threshold. The second threshold has a higher HU valuethan the first threshold. In an operation 706, one or more CCs in thefirst binary volume that intersect voxels from the second binary volumeare selected as the one or more vessel CCs (VCCs). In an exemplaryembodiment, intersection may be determined based on a spatial proximitybetween the CCs. In an exemplary embodiment, the first threshold and thesecond threshold are selected based on a statistical analysis of theinput data such that the amount of voxels above the first threshold isapproximately 0.15% of the total number of voxels in the volume and suchthat the amount of voxels above the second threshold is approximately0.45% of the total number of voxels.

In an operation 708, the selected VCCs are labeled in the first binaryvolume. In an operation 710, a starting point or root is selected for afirst VCC. In an operation 712, a width map is calculated for the firstVCC. The width map includes the width of the VCC or the inverse distancefrom any point in the VCC to the boundary of the VCC. Thus, small valuesare near the centerline of the VCC and larger values are at the edges ofthe VCC. In an operation 714, the exit point of the selected VCC isidentified in the binary volume. The right coronary artery tree has asingle exit point. Additionally, the left main artery tree has a singleexit point. In an operation 716, a distance map is calculated for thefirst VCC. The distance is calculated from any point in the VCC to theidentified exit point. The distance map includes the calculated distanceweighted by the width to ensure that the minimal path follows the vesselcenterline. In an operation 718, candidate endpoints are identified. Forexample, during the calculation of the weighted distance map, one ormore candidate endpoints may be saved. The candidate endpoints arevoxels, which did not update any of their neighbors during the distancemap calculation. In an operation 720, non-local maxima candidateendpoints are filtered. Thus, the candidate endpoints are scanned andonly local maxima with respect to the distance from the root over agiven window are kept. This process eliminates candidates that are nottrue blob vessel end points.

In an operation 722, an identifier for the candidate endpoint iscreated. In an operation 724, a new vertex is added to a symbolic graphof the vessel tree. In an operation 726, a first candidate endpoint isbacktracked to the root to create graph edges. An auxiliary volume isused to mark voxels that have already been visited. The back-trackingmay be a gradient descent iterative process (the gradient is in thedistance field). Because the weighted distance map contains a singleglobal minimum (the root), convergence is guaranteed. The method used todefine the distance map ensures that the backtracking will be along thecenterline or close to it. In an operation 728, all visited voxels inthe auxiliary volume are marked with the current vertex identifierduring the backtracking.

In an operation 730, a determination is made concerning whether or not aroot is reached. If a root is reached, processing continues at anoperation 732. If a root is not reached, processing continues at anoperation 734. In operation 732, a new edge and vessel path are definedbased on the backtracking Processing continues at an operation 740. Inan operation 734, a determination is made concerning whether or not analready visited voxel is reached. If an already visited voxel isreached, processing continues at an operation 736. If an already visitedvoxel is not reached, processing continues at an operation 726 tocontinue the backtracking to the endpoint. In an operation 736, a newedge and a new bifurcation vertex are defined. In an operation 738, thenew bifurcation vertex is connected to the currently backtracked path,and the new edge and the new bifurcation vertex are added to the vesseltree.

In an operation 742, a determination is made concerning whether or notthe endpoint is a leaf of the vessel tree or a vessel disconnected dueto a low vesselness measure. In an exemplary embodiment, thedetermination is made based on the direction of the vessel at theendpoint and by searching for another VCC in a vacancy that contains anearby endpoint. If the endpoint is a leaf, processing continues atoperation 722 to create a new graph. The two graphs are joined together.If the endpoint is not a leaf, processing continues at an operation 744.In an operation 744, a determination is made concerning whether or notthe last endpoint has been processed. If the last endpoint has not beenprocessed, processing continues at operation 722.

If the last endpoint has been processed, processing continues at anoperation 746. In operation 746, short branches are removed based on therationale that they do not contribute to the analysis because importantfindings are usually located at the major blood vessels, which are thickand elongated. Therefore, in an exemplary embodiment, graph edges whichlead to endpoints that are less than a length threshold are removed. Anexemplary length threshold is 5 mm. In an operation 748, x-junctions areremoved to eliminate veins. Veins are usually faint and spatially closeto the arteries. X-junctions are defined as two very close bifurcationpoints. For example, close bifurcation points may be less thanapproximately 3 mm from each other. In an exemplary embodiment, abifurcation may be two VCCs intersecting at angles between approximately70 degrees and approximately 110 degrees. Additionally, a bifurcationmay be two VCCs intersecting at angles approximately equal to 90degrees. The sub-tree which remains is the one which has the closestdirection to the edge arriving from the aorta.

For example, with reference to FIG. 10, a vessel tree 1000 includes afirst vessel path 1002, a second vessel path 1004, and a third vesselpath 1006. First vessel path 1002 and second vessel path 1004 form afirst x-junction 1008. First vessel path 1002 has the closest directionto the edge arriving from the aorta and is selected to remain in thevessel tree. Second vessel path 1004 is removed. First vessel path 1002and third vessel path 1004 form a second x-junction 1010. Again, firstvessel path 1002 has the closest direction to the edge arriving from theaorta and is selected to remain in the vessel tree. Third vessel path1006 is removed. In an operation 750, single entry, single exit pointvertices are removed. These vertices are created when one of theendpoints is recursively continued. The vertex is removed, and the twoedges are joined to a single path.

With reference again to FIG. 2, in an operation 244, the definedcoronary artery vessel tree is labeled. A list of graph edges (vesselsegments) may be assigned to each blood vessel tracked by analyzing therelative section positions and locations relative to detected anatomicalheart landmarks. The blood vessel tree represented by a centerline foreach blood vessel segment is stored at computing device 102.

In an operation 246, a radius of each blood vessel is determined. In anoperation 248, a blood vessel centerline is determined. “Sausages” ofblood vessels are obtained from the coronary artery vessel tree. Each“sausage” includes axial blood vessel cross-sections taken perpendicularto the blood vessel direction. Initially, a blood vessel center ispresumed to be at the center of each section. Either “stretched” or“curved” blood vessels can be used. Any blood vessel radius and centerline estimation method can be used. In an exemplary embodiment, low passpost-filtering between consecutive cross-sections is performed. Becausea blood vessel may be surrounded by tissue having similar attenuationvalues, indirect indicators may be used to define the blood vessel edge.Areas having low values, which clearly don't belong to a blood vesselare identified, and the largest circle that lies outside the identifiedareas is defined. In an alternative embodiment, a largest circle thatcan be defined that fits into the valid (bright) area is defined. Anarbitration process may be used to determine which approach should beused for each blood vessel. A blood vessel center consisting of a numberof pixels can be defined, in particular when a cross section iselongated. In an exemplary embodiment, the blood vessel center isreduced to a single pixel.

In an operation 250, areas of calcium are identified in each bloodvessel. Any high precision calcium identification method can be used. Inan exemplary embodiment, a cross section based analysis aimed atlocation of the calcium seeds is performed, and a volume based analysisaimed at removal of spurious seeds created by the “salt noise” and bythe growing of valid seeds into the correct calcium area is performed.With reference to FIG. 8, exemplary operations associated withidentifying the areas of calcium, if any, in each blood vessel aredescribed in accordance with an exemplary embodiment. Additional, fewer,or different operations may be performed, depending on the embodiment.The order of presentation of the operations is not intended to belimiting. In an operation 800, a first slice is selected from the heartregion data. In an operation 802, a calcium threshold is applied to thefirst slice. In an exemplary embodiment, the calcium threshold is 150intensity levels above the blood vessel lumen level. Adaptive thresholdvalues taking into account expected lumen values are used. In anoperation 804, a morphological filter is applied to the thresholdedfirst slice based on the non-concentric nature of the calcium deposits.Empirical observations indicate that calcium tends to appear close tothe blood vessel borders.

In an operation 806, a maximum intensity in a given cross-section isidentified as a possible location of a Calcium seed. In an operation808, a distance from the center to the maximum intensity is calculated.In an operation 810, a determination is made concerning whether or notthe calculated distance exceeds a calcium distance threshold. Thecalcium distance threshold is based on a comparison with an estimatedradius value. If the distance does not exceed the calcium distancethreshold, processing continues in an operation 814. If the distancedoes exceed the calcium distance threshold, processing continues in anoperation 812. In operation 812, an area of the calcium seed iscalculated. In operation 814, a determination is made concerning whetheror not any vessels remain for processing. If vessels remain, processingcontinues at operation 808. If no vessels remain, processing continuesat an operation 816. In operation 816, a determination concerningwhether or not any slices remain for processing is performed. If noslices remain, processing continues at an operation 820. If slicesremain, processing continues at an operation 818. In operation 818, thenext slice is selected from the heart region data and processingcontinues at operation 802. In operation 820, a volume of any identifiedcalcium seed(s) is calculated based on the area calculated for eachslice and the number of slices over which the identified calcium seed(s)extends. If a calcium seed is identified, it also is extended to thesurrounding high intensity areas providing that no “spill to the center”occurs. An extent of the calcium seed may be determined based on athreshold. For example, lumen intensities exceeding approximately 650 HUmay be considered to be calcified plaque or part of the Calcium seed.

With reference again to FIG. 2, in an operation 252, areas of softplaque are identified in each blood vessel by the low intensity insidethe blood vessel area. Any high precision soft plaque identificationmethod can be used. With reference to FIG. 9, exemplary operationsassociated with identifying the areas of soft plaque, if any, in eachblood vessel are described in accordance with an exemplary embodiment.Additional, fewer, or different operations may be performed, dependingon the embodiment. The order of presentation of the operations is notintended to be limiting. In an operation 900, a first slice is selectedfrom the heart region data. In an operation 902, a soft plaque thresholdis applied to the first slice. In an exemplary embodiment, the softplaque threshold is between approximately 50 HU and approximately 200HU. Adaptive threshold values taking into account expected lumen valuesare used in an exemplary embodiment. In an operation 904, adetermination is made concerning whether or not calcium is present. Thepresence of calcium may indicate the presence of the frequent figure “8”shaped pattern. In the figure “8” shaped pattern, calcium is located inone of the ovals of the “8”. A lumen is located in the other oval of the“8”, and soft plaque connects the two ovals. If calcium is present,processing continues at an operation 906. If calcium is not present,processing continues at an operation 908. In operation 906, a softplaque area is identified and processing continues at an operation 911.

In operation 908, a determination is made concerning whether or not ahalf-moon structure is located in the blood vessel lumen. If a half-moonstructure is identified from the determination, processing continues atan operation 910. If a half-moon structure is not identified from thedetermination, processing continue at operation 911. In operation 910, asoft plaque area is identified. In operation 911, an area of theidentified soft plaque is calculated. In operation 912, a determinationconcerning whether or not any slices remain for processing is performed.If no slices remain, processing continues at an operation 916. If slicesremain, processing continues at an operation 914. In operation 914, thenext slice is selected from the heart region data, and processingcontinues at operation 902. In operation 916, a volume of any identifiedsoft plaque area(s) is calculated based on the area calculated for eachslice and the number of slices over which the identified soft plaquearea(s) extends. In an operation 918, a volume of any identified calciumseed(s) is updated to include areas between the calcium seed and theblood vessel border and between the calcium and soft plaque areas tocompensate for natural intensity low passing that may have occurredduring the CT image acquisition.

With reference again to FIG. 2, in an operation 254, a severity of anyobstructions identified containing soft plaque or calcium is calculated.Any obstruction computation method can be used. In an exemplaryembodiment, a total obstruction ratio is calculated as a ratio of thetotal calcium and soft plaque areas divided by the total blood vesselarea excluding the border area. In an exemplary embodiment, anobstruction is identified to be severe if the total obstruction ratioexceeds 50% for at least two consecutive cross sections. An examiningphysician may be allowed to control the threshold to achieve a systemsensitivity matching their clinical requirements.

In some pathological cases, the cross section images may appearreasonably normal. In these cases, pathology must be identified based onthe analysis of global variations. In an operation 256, global filtersare applied to identify pathologies. For example, a first filter may beapplied to identify a rapid decrease in the blood vessel radius. Asecond filter may be applied to identify a rapid decrease in the lumenintensity. A third filter may be applied to identify a rapid increase inthe lumen intensity. The decisions from the series of filters may becumulative. As a result, it is sufficient if a pathology is identifiedthrough use of one of the three filters. The filters may use the valuesof blood vessel radius and luminance as computed above. Use of theglobal filters takes into account that even healthy vessels featuresignificant radius and luminance variations in particular due to naturalnarrowing of the blood vessels, rapid changes in the vicinity ofbifurcations (especially after the bifurcations), noise (in particularfor relatively narrow vessels), etc. Anomalies identified by the globalfilters are discarded, if located in the vicinity of any bifurcations.

The operations described with reference to FIGS. 2-9 have been appliedto a set of 50 clinical patient studies. The same studies were analyzedby expert radiologists. Overall, 200 blood vessels were analyzed. Out ofthis benchmark, 42 cases were identified as having severe pathologies.The remaining 158 cases were deemed to have no pathologies or onlymoderate pathologies. Pathology identification application 112identified all of the severe cases correctly with a false alarm rate of11%.

With reference again to FIG. 2, in an operation 258, a summary report isprovided to a user based on the processes described with reference toFIGS. 2-9. With reference to FIG. 11, a first user interface 1100 ofvisualization application 114 is shown. First user interface 1100 mayinclude a header portion 1102. Header portion 1102 may include patientdata, study data, and/or acquisition data. For example, data displayedin header portion 1102 may be obtained from a header of the DICOM data.First user interface 1100 further may include a blood vessel listportion 1104. Blood vessel list portion 1104 may include a list of theblood vessels in the created blood vessel tree. Displayed next to a nameidentifying each blood vessel may be information related to each bloodvessel including a lumen status, a total number of lesions, a number ofcalcium lesions, and a number of soft plaque lesions. The lumen statusmay indicate “normal” or a percentage of blockage that may be apercentage range. If a plurality of lesions are present, the range mayindicate the maximum blockage range. A maximum volume and Agatston scoremay be displayed for the calcium lesions. A maximum volume and scorealso may be displayed for the soft plaque lesions.

User selection of a blood vessel 1106 in blood vessel list portion 1104may cause display of a detailed description of the lesions associatedwith the selected blood vessel in a detail portion 1108. Detail portion1108 may include a list of the lesions. For each lesion, a segment name,a lesion type, a degree of stenosis value, a volume, a distance from theaorta, a distance from the blood vessel origin, an eccentricity, adegree of positive remodeling, and a morphological regularity may beshown. First user interface 1100 further may include a totals portion1110. Totals portion 1110 may include summary data associated with adegree of stenosis, lesions, the number of stents, etc.

With reference again to FIG. 2, in an operation 260, a visualization ofthe blood vessels is provided to a user based on the processes describedwith reference to FIGS. 2-9. With reference to FIG. 12, a second userinterface 1200 of visualization application 114 in accordance with afirst exemplary embodiment is shown. In the exemplary embodiment of FIG.12, four simultaneous views of the same pathology may be shown tofacilitate a correct diagnosis with each view presented in a differentarea of second user interface 1200. Each view may be created using avariety of graphical user interface techniques in a common window, inseparate windows, or in any combination of windows. Second userinterface 1200 may include a first axial slice viewer 1202, a first 3-Dcoronary vessel map 1204, a first stretched blood vessel image 1206, andan intra-vascular ultrasound (IVUS) type view 1208. First axial sliceviewer 1202 presents intensity levels from a slice of imaging data. Theintensity levels may be indicated in color or gray-scale. For example,first axial slice viewer 1202 may indicate an identified pathology 1203in red. First axial slice viewer 1202 may present the slice of imagingdata in a top left area of second user interface 1200.

First 3-D coronary vessel map 1204 provides a view of the blood vesseltree synchronized with first axial slice viewer 1202 to indicate theidentified pathology 1203. First 3-D coronary vessel map 1204 may bepresented in a top right area of second user interface 1200 and mayinclude a 3-D grid to identify the length of the blood vessels in theblood vessel tree in each direction. Selecting an area of firststretched blood vessel image 1206 may cause image rotation of first 3-Dcoronary vessel map 1204 around its axis to facilitate a correct 3-Dperception of the blood vessel structure. First 3-D coronary vessel map1204 may be synchronized with first axial slice viewer 1202 todistinguish the selected blood vessel from the remaining blood vesselsin the blood vessel tree. First 3-D coronary vessel map 1204 may berotated using an input interface as known to those skilled in the art.Indicators may be provided in first 3-D coronary vessel map 1204 toindicate end points and bifurcations. For example, end points may beindicated using green circles and bifurcations may be indicated usingred circles.

First stretched blood vessel image 1206 includes a vertical bar whichdenotes a location of the slice displayed in first axial slice viewer1202 in a stretched view of a selected blood vessel. First stretchedblood vessel image 1206 may be located in a bottom left area of seconduser interface 1200. The physician can superimpose corresponding plaqueareas. For example, soft plaque may be indicated in red and calcifiedplaque indicated in blue. If desired, the physician can invoke an editmode and correct automatic results.

With reference to FIG. 13, a third user interface of visualizationapplication 114 graphically displays a lumen area of each blood vesselto clearly identify all stenosis lesions and to allow an evaluation oftheir severity. The graphical display includes a distance from the aortaon the X-axis and a lumen area on the Y-axis. A first curve 1300indicates a normal blood vessel lumen. A second curve 1302 indicates astenosis due to calcified plaque. A third curve 1304 indicates astenosis due to soft plaque.

With reference to FIG. 14, a fourth user interface 1400 of visualizationapplication 114 in accordance with a second exemplary embodiment isshown. Fourth user interface 1400 may include a second axial sliceviewer 1402, a second 3-D coronary vessel map 1404, a second stretchedblood vessel image 1406, and a pathology report type view 1408. Secondaxial slice viewer 1402 may include an axial slice of the imaging datapresented in a top left area of second user interface 1400. provides acurrent location on the 3-D coronary vessel map synchronized with secondaxial slice viewer 1402. Second 3-D coronary vessel map 1404 may bepresented in a top right area of second user interface 1400 and mayinclude a 3-D grid to identify the length of the blood vessels in theblood vessel tree in each direction. Selecting an area of second 3-Dcoronary vessel map 1404 may cause image rotation around its axisfacilitating a correct 3-D perception of the blood vessel structure.

Second stretched blood vessel image 1406 includes a vertical bar whichdenotes a location of the slice displayed in second axial slice viewer1402 in a stretched view of a selected blood vessel. Second stretchedblood vessel image 1406 may be presented in a bottom left area of seconduser interface 1400.

Pathology report type view 1408 may contain a pathology list 1409 ofdetected pathologies based on the processes described with reference toFIGS. 2-9. The pathologies may include soft plaque, calcified plaque,and mixed plaque regions. The location and stenosis level may beincluded for each pathology in pathology list 1409. Selecting apathology 1410 from pathology list 1409 of pathology report type view1408 may cause a synchronized display of pathology 1410 in second axialslice viewer 1402, second 3-D coronary vessel map 1404, and secondstretched blood vessel image 1406. For example, second axial sliceviewer 1402 includes a first pathology indicator 1412, which indicatesthe location of pathology 1410 in second axial slice viewer 1402. Second3-D coronary vessel map 1404 includes a second pathology indicator 1414,which indicates the location of pathology 1410 in the 3-D coronaryartery tree view. Second stretched blood vessel image 1406 includes afirst point 1416 and a second point 1418, which indicate the location ofpathology 1410 in second stretched blood vessel image 1406.

With reference to FIG. 15, a fifth user interface 1500 of visualizationapplication 114 in accordance with a third exemplary embodiment isshown. Fifth user interface 1500 may include a third axial slice viewer1502 and a third stretched blood vessel image 1504. Third axial sliceviewer 1502 is synchronized with third stretched blood vessel image1504. Third axial slice 1502 presents intensity levels from an axialslice of imaging data. The intensity levels may be indicated in color orgray-scale. For example, third axial slice viewer 1502 may indicate anidentified pathology 1506 in red. Third stretched blood vessel image1504 presents intensity levels of a blood vessel selected from thirdaxial slice viewer 1502 and shown in stretched form. Third stretchedblood vessel image 1504 may includes a vertical bar 1508 which denotes alocation of the slice presented in third axial slice viewer 1502. Whenthe user selects a vessel area in third axial slice viewer 1502, thirdstretched blood vessel image 1504 shows a stretched presentation of theappropriate vessel. When the user selects an area in third stretchedblood vessel image 1504, third axial slice viewer 1502 displays theappropriate slice of the patient study.

In an exemplary embodiment, fifth user interface 1500 may initiallyinclude third axial slice viewer 1502. When the user selects an arteryfrom third axial slice viewer 1502, the selected blood vessel ispresented in third stretched blood vessel image 1504 with vertical bar1508 denoting the location of the slice presented in third axial sliceviewer 1502. Execution of one or more of the processes described withreference to FIGS. 2-9 may be performed after selection of the bloodvessel to identify the stretched blood vessel presented in thirdstretched blood vessel image 1504. As a result, using a single “click”the user may trigger a determination of all relevant segments of theblood vessel from the slices and reconstruct the stretched blood vesselfor presentation in third stretched blood vessel image 1504.

Fifth user interface 1500 further may include an axial presentation onlybutton 1510, a new study selection button 1512, a save current screenbutton 1514, and an exit program button 1516. User selection of axialpresentation only button 1510 causes stretched blood vessel image 1504to be removed from fifth user interface 1500. User selection of newstudy selection button 1512 causes presentation of a selection windowthat allows the user to select a new patient study for analysis. Userselection of save current screen button 1514 causes presentation of asave window that allows the user to select a location and a name for afile to which the contents of fifth user interface 1500 are saved forreview, for printing, for sending with a message, etc. User selection ofexit program button 1516 may cause fifth user interface 1500 to close.

The foregoing description of exemplary embodiments of the invention havebeen presented for purposes of illustration and of description. It isnot intended to be exhaustive or to limit the invention to the preciseform disclosed, and modifications and variations are possible in lightof the above teachings or may be acquired from practice of theinvention. The functionality described may be implemented in a singleexecutable or application or may be distributed among modules thatdiffer in number and distribution of functionality from those describedherein. Additionally, the order of execution of the functions may bechanged depending on the embodiment. The embodiments were chosen anddescribed in order to explain the principles of the invention and aspractical applications of the invention to enable one skilled in the artto utilize the invention in various embodiments and with variousmodifications as suited to the particular use contemplated. It isintended that the scope of the invention be defined by the claimsappended hereto and their equivalents.

1. A method of identifying aorta exit points from imaging data, themethod comprising: identifying an aorta object from imaging data;calculating a vesselness score for a plurality of voxels in a ringaround the identified aorta object; applying a first threshold to thecalculated vesselness score for the plurality of voxels; identifying apossible exit point based on the applied first threshold; if greaterthan one possible exit point is identified, identifying a closestpossible exit point to the aorta object; and if greater than one closestpossible exit point is identified, identifying an exit point byselecting the closest possible exit point having a maximum width.
 2. Themethod of claim 1, wherein identifying the aorta object comprises: (a)selecting a slice of data from the imaging data; (b) applying a secondthreshold to the selected slice of data to define voxels above thesecond threshold; (c) identifying a connected component from the definedvoxels; (d) eliminating the identified connected component fromconsideration as the aorta object if a size value of the identifiedconnected component is below a size threshold; (e) if the identifiedconnected component is not eliminated, comparing the identifiedconnected component with an aorta candidate object identified in aprevious slice of data; (f) assigning the identified connected componentto the aorta candidate object based on a matching comparison; (g)creating a new aorta candidate object based on a non-matchingcomparison; and (h) repeating (a)-(g) for a plurality of slices toidentify aorta candidate objects; and (i) selecting the aorta objectfrom the identified aorta candidate objects.
 3. The method of claim 2,wherein comparing the identified connected component with the aortacandidate object comprises: calculating a center of the identifiedconnected component; comparing the calculated center with a center ofthe aorta candidate object calculated from a previous slice; and if thecalculated center of the identified connected component is within anumber of pixels of the center of the aorta candidate object, theidentified connected component is determined to match the aortacandidate object.
 4. The method of claim 3, further comprising: if thecalculated center of the identified connected component is not withinthe number of pixels of the center of the aorta candidate object, theidentified connected component is determined not to match the aortacandidate object.
 5. The method of claim 3, wherein the number of pixelsis twenty.
 6. The method of claim 2, wherein the first threshold isapproximately 200 Hounsfeld units.
 7. The method of claim 2, furthercomprising eliminating the identified connected component fromconsideration as the aorta object if a compactness value of theidentified connected component indicates the identified connectedcomponent is too compact to be an aorta object.
 8. The method of claim2, further comprising applying a morphological filter to the definedvoxels.
 9. The method of claim 2, wherein the selected aorta object isclosest to an upper, left corner of the imaging data.
 10. The method ofclaim 2, further comprising, after (h), eliminating a candidate from theidentified aorta candidate objects that has a length less than a numberof slices.
 11. The method of claim 10, wherein the number of slices istwenty.
 12. The method of claim 1, further comprising filtering anidentified possible exit point from consideration as the exit pointbased on a size value of the identified possible exit point.
 13. Themethod of claim 1, further comprising filtering an identified possibleexit point from consideration as the exit point based on an incidentangle of the identified possible exit point relative to the aortaobject.
 14. The method of claim 1, further comprising filtering anidentified possible exit point from consideration as the exit pointbased on a location of the identified possible exit point relative tothe aorta object.